Balance protection method and system for multiple series-connected battery cells

Abstract

The present invention relates to the field of battery balancing protection technology, and in particular to a balancing protection method and system for multiple series-connected battery cells. The method comprises the following steps: collecting multi-node battery operation status data of multiple series-connected battery cells and performing series gradient influence analysis to obtain internal resistance change series gradient influence data; calculating performance attenuation gradient based on internal resistance change series gradient influence data to obtain thermal damage performance attenuation gradient data; formulating a series-connected battery cell balancing intervention strategy based on the thermal damage performance attenuation gradient data to obtain a battery cell balancing intervention strategy; performing automated firmware design based on the battery cell balancing intervention strategy to obtain a balancing intervention strategy firmware; and embedding the balancing intervention strategy firmware into a battery cell management system to perform battery cell balancing protection. The present invention makes the battery balancing protection technology more perfect by optimizing the battery balancing protection technology.

Description

Technical Field

[0001] The present invention relates to the technical field of battery equalization protection, and in particular to a method and system for equalization protection of multiple series-connected battery cells. Background Art

[0002] The balancing protection of multiple series-connected battery cells is mainly used to manage and optimize the performance of battery packs, especially in large-scale energy storage systems, electric vehicles, and renewable energy systems. In these applications, multiple battery cells are connected in series to achieve the required high voltage and capacity. Due to the inherent differences in battery cells, such as manufacturing processes, chemical properties, usage conditions, and aging processes, the voltage and state between different cells will gradually become inconsistent. If this inconsistency is not effectively controlled, it may lead to a decline in the performance of the entire battery pack, and even cause safety issues such as overcharging or over-discharging. Therefore, battery balancing technology has become the key to ensuring the reliability and life of the battery pack. The balancing protection methods are mainly divided into two categories: passive balancing and active balancing. Passive balancing consumes excess energy on cells with high battery voltage to make them consistent with the voltage of other cells. Although it is low-cost, it is inefficient; active balancing maintains the voltage balance between cells by transferring energy from high-voltage cells to low-voltage cells. Although it is efficient, the system is complex and costly. Modern balancing systems also combine intelligent control algorithms and battery management systems (BMS) to monitor and adjust the voltage of each cell in real time to further optimize the performance and safety of the battery pack. However, a conventional method for balancing and protecting multiple battery cells in series has the problems of inaccurate analysis of the performance degradation of the battery cells in series and inaccurate evaluation of the heat loss effect. Summary of the invention

[0003] Based on this, it is necessary to provide a balancing protection method and system for multiple battery cells connected in series to solve at least one of the above technical problems.

[0004] To achieve the above object, a method for balancing and protecting multiple battery cells connected in series is provided, the method comprising the following steps:

[0005] Step S1: collecting multi-node battery operation status data of multiple series-connected battery cells to obtain multi-node battery operation status data; performing series gradient influence analysis on the multi-node battery operation status data to obtain internal resistance change series gradient influence data;

[0006] Step S2: simulating the thermal damage gain effect according to the series gradient influence data of the internal resistance change to obtain the thermal damage gain effect data; calculating the performance attenuation gradient according to the thermal damage gain effect data to obtain the thermal damage performance attenuation gradient data;

[0007] Step S3: formulating a battery cell balancing intervention strategy based on the thermal damage performance attenuation gradient data to obtain a battery cell balancing intervention strategy;

[0008] Step S4: performing automated firmware design based on the battery cell balancing intervention strategy to obtain balancing intervention strategy firmware; embedding the balancing intervention strategy firmware into the battery cell management system to perform balancing protection of the battery cells.

[0009] The present invention acquires a comprehensive battery working state by real-time collection of operating state data of multiple series-connected battery cells, including parameters such as voltage, current, and temperature, analyzes the collected data, and pays special attention to the change of internal resistance. Internal resistance is an important indicator of battery health status, and its change can affect the performance and life of the battery. Detecting the change of internal resistance gradient can help to find potential problems or signs of failure in the battery early. According to the internal resistance gradient data, the remaining life of the battery can be more accurately evaluated to avoid unexpected shutdown or performance degradation caused by unexpected internal resistance changes. Thermal damage is one of the important reasons for battery performance degradation. Simulation analysis can help predict the change of internal temperature of the battery under different working conditions and its specific impact on performance. According to the simulation results, the working parameters or working strategies of the battery can be adjusted to minimize the negative impact of thermal damage on battery performance, determine the decay rate of battery performance under different conditions, and provide a basis for formulating appropriate maintenance plans and periodic inspections. Through periodic performance decay gradient calculation, the management strategy of the battery system can be continuously optimized to extend its reliable working time and life. According to the real-time monitoring data, the balancing operation of the battery core is adjusted to ensure that the charge state and voltage level between each battery cell remain balanced. According to different working conditions and ambient temperatures, the balancing intervention strategy is optimized to maximize the energy storage efficiency and power output stability of the battery system. Through precise balancing intervention strategies, the imbalance caused by excessive voltage differences between battery cells can be avoided. The balancing operation can reduce overcharging or overdischarging of the battery cells and reduce the risk of damage to the battery system. Effective balancing management can extend the service life of the battery and reduce the performance degradation and early replacement costs caused by battery imbalance. An algorithm that can automatically adjust the state of the battery cell according to the preset balancing strategy is developed, and the designed firmware is verified and optimized for security to ensure that the firmware can operate reliably under various working conditions. The working state of the battery cell can be monitored in real time, and the operation can be automatically adjusted according to the preset balancing strategy to maintain the stability and long-term performance of the battery system. The automated firmware design reduces the reliance on manual intervention and improves the autonomy and response speed of the management system. Therefore, the present invention provides a balancing protection method for multiple battery cells connected in series, which is an optimization of a traditional balancing protection method for multiple battery cells connected in series, and solves the problems of inaccurate performance attenuation analysis of the battery cells connected in series and inaccurate evaluation of heat loss effects in the traditional balancing protection method for multiple battery cells connected in series, and improves the accuracy of the performance attenuation analysis of the battery cells connected in series and the accuracy of the evaluation of the heat loss effects.

[0010] Preferably, step S1 comprises the following steps:

[0011] Step S11: using distributed sensors to collect multi-node battery operation status data of multiple series-connected battery cells to obtain multi-node battery operation status data;

[0012] Step S12: performing data cleaning on the multi-node battery operation status data to obtain multi-node battery operation status cleaning data;

[0013] Step S13: mapping the internal resistance change according to the multi-node battery operation status cleaning data to obtain multi-node internal resistance change mapping data;

[0014] Step S14: performing series gradient influence analysis on the multi-node internal resistance change mapping data to obtain internal resistance change series gradient influence data.

[0015] The present invention can monitor various operating status data of multiple series-connected battery cells in real time through distributed sensors, such as voltage, current, and temperature. The distributed sensors can realize comprehensive and real-time data collection of the battery cells, ensure the accuracy and timeliness of the acquired data, cover different positions and multiple nodes of the battery cells, provide comprehensive battery operating status information, and help identify potential problems and optimize management strategies; the collected raw data is cleaned and preprocessed to ensure data quality and consistency, unify data formats and units, and make comparison and analysis between different data sources more convenient and feasible; the cleaned data is used for internal resistance change mapping to accurately reflect the change trend of the battery cell internal resistance over time; the internal resistance is a key indicator for evaluating the battery health status; the mapping data can help accurately evaluate the battery life and health status, and provide a basis for predictive maintenance; according to the trend of the internal resistance change, timely measures can be taken to avoid battery failure and performance degradation; based on the mapping data, the gradient influence of the battery cell internal resistance in the series structure is analyzed, and the gradient of the internal resistance change of different nodes is understood, which is helpful to formulate an optimized battery balancing management strategy, ensure that the battery of each node works in the best state, identify the node with a large internal resistance change, warn of failure in advance, and optimize the battery use and maintenance strategy to extend the battery life.

[0016] Preferably, step S2 comprises the following steps:

[0017] Step S21: performing multi-node temperature change matching according to the internal resistance change series gradient influence data to obtain multi-node temperature change data;

[0018] Step S22: simulating the thermal damage gain effect according to the internal resistance change series gradient influence data and the multi-node temperature change data to obtain the thermal damage gain effect data;

[0019] Step S23: performing gain effect quantification processing on the thermal damage gain effect data to obtain thermal damage gain effect quantification data;

[0020] Step S24: Calculate the performance attenuation gradient based on the quantified data of the thermal damage gain effect to obtain thermal damage performance attenuation gradient data.

[0021] The present invention uses the gradient influence data of internal resistance change to analyze the internal resistance change of different nodes and match the corresponding temperature change data. Temperature is one of the important factors affecting battery performance and life. By matching temperature data, the thermal management state of the battery can be more accurately evaluated. Combined with temperature change data, the relationship between internal resistance change and temperature can be comprehensively considered, providing a basis for subsequent thermal damage gain effect simulation; combined with the gradient influence data of internal resistance change and temperature change data, a simulation analysis of thermal damage gain effect is performed. Thermal damage refers to the performance attenuation and shortened life of the battery caused by temperature change. The simulation can predict the degree of thermal damage of batteries at different nodes; according to the simulation results, a more reasonable battery use and management strategy can be formulated to avoid performance degradation and failure risks caused by excessive temperature. The simulation results of the thermal damage gain effect are quantified to obtain specific data indicators. The simulation results are converted into specific data indicators, such as thermal damage degree, expected performance attenuation speed, etc., which helps engineers and managers understand the actual state and potential risks of the battery. After providing quantitative data, cost-benefit analysis, maintenance plan formulation and other decisions can be made more accurately to ensure the long-term stability and economy of the battery system. The performance attenuation gradient of batteries at different nodes is calculated using the quantified data of the thermal damage gain effect. By calculating the performance attenuation gradients of different nodes, the life and performance change trends of the battery in actual use can be predicted. Based on the gradient data, a refined maintenance plan and battery replacement strategy can be formulated to maximize the efficiency and cost-effectiveness of the battery system.

[0022] Preferably, step S22 includes the following steps:

[0023] Step S221: Calculating the heat capacity storage efficiency between different nodes according to the temperature change data of multiple nodes to obtain heat capacity storage efficiency data;

[0024] Step S222: Calculating the heat transfer coefficients between different nodes based on the heat capacity storage efficiency data to obtain the node heat transfer coefficients;

[0025] Step S223: performing node thermal sensitivity quantification processing according to the thermal capacity storage efficiency data and the node heat transfer coefficient to obtain node thermal sensitivity quantification data;

[0026] Step S224: evaluating the node thermal damage impact range degree according to the node thermal sensitivity quantified data and the node heat transfer coefficient, and obtaining the node thermal damage impact range data;

[0027] Step S225: calculating the internal resistance failure probability of the internal resistance change series gradient influence data according to the node thermal damage influence range data to obtain the internal resistance failure probability data;

[0028] Step S226: Perform a thermal damage gain effect simulation based on the node thermal damage influence range data and the internal resistance failure probability data to obtain thermal damage gain effect data.

[0029] The present invention calculates the heat capacity storage efficiency of different nodes based on the temperature change data of multiple nodes. The heat capacity storage efficiency reflects the ability of each node to absorb and release heat, helps to determine the performance of the node when the temperature changes. According to the calculation results, the thermal management strategy can be adjusted to ensure that the temperature of the node is controlled within a safe and effective range when working; based on the heat capacity storage efficiency data, the heat transfer coefficient between different nodes is calculated. The heat transfer coefficient reflects the rate and efficiency of heat transfer between nodes, and helps to evaluate the heat flow inside the system. According to the transfer coefficient, a more effective thermal management system can be designed to reduce the efficiency loss and thermal damage risk caused by thermal coupling. Combined with the heat capacity storage efficiency and the heat transfer coefficient, the thermal sensitivity of the node is quantified. By quantifying the thermal sensitivity, it is determined which nodes in the system are particularly sensitive to temperature changes, which will cause performance degradation or damage. According to the results, the thermal management measures can be strengthened or optimized in a targeted manner to improve the stability and reliability of the system. According to the quantified data of the thermal sensitivity and the heat transfer coefficient, the scope of influence of the node thermal damage on the system is evaluated. Determine how the thermal damage spreads in the system and other nodes or system components that may be affected. According to the evaluation results, preventive and response strategies are formulated to reduce the negative impact of thermal damage on the performance and reliability of the entire system. Using the node thermal damage impact range data, calculate the failure probability of the internal resistance under different conditions. The change of internal resistance directly affects the performance and life of the battery. The failure probability calculation can help predict the stability and reliability of the battery system in long-term use. According to the failure probability, optimize the battery maintenance plan and replacement strategy to extend the life of the battery system and reduce maintenance costs. Combined with the node thermal damage impact range data and the internal resistance failure probability, simulate the gain effect of thermal damage on performance in the system. Through simulation, the performance attenuation trend of the system under different temperatures and usage conditions can be predicted, and measures can be taken in advance to avoid potential problems. According to the simulation results, optimize the design and management strategy of the battery system to ensure the stability and reliability of the system in various environments.

[0030] Preferably, calculating the internal resistance failure probability of the internal resistance change series gradient influence data comprises the following steps:

[0031] The influence range difference between different nodes is calculated for the node thermal damage influence range data to obtain the influence range difference data;

[0032] Perform thermal interaction internal resistance influence correction on the internal resistance change series gradient influence data according to the influence range difference data to obtain internal resistance change series correction data;

[0033] Calculate the node internal resistance failure limit based on the impact range difference data and the internal resistance change series correction data to obtain the node internal resistance failure limit data;

[0034] The cross-validation method is used to perform data cross-segmentation validation on the node internal resistance failure limit data to obtain the internal resistance failure limit segmentation validation data;

[0035] Based on the law of large numbers, the internal resistance failure probability is calculated for the internal resistance failure limit segmentation verification data to obtain the internal resistance failure probability data.

[0036] According to the node thermal damage influence range data, the present invention calculates the influence range difference between different nodes, analyzes the thermal damage propagation characteristics between different nodes, determines the influence degree of each node under temperature change, and the influence range difference data provides key information for the thermal interaction correction in the subsequent steps; using the influence range difference data, the internal resistance change series gradient influence data is thermally corrected, considering the difference in thermal damage influence range between different nodes, and correcting the gradient data of the internal resistance change to make it closer to the actual working environment. The corrected data can better reflect the probability of actual internal resistance change, provide more accurate input for the next step of failure limit calculation, and combine the influence range difference data and the corrected internal resistance change data to calculate the limit conditions of internal resistance failure of each node. According to the calculation results, the possibility of internal resistance failure of each node under given conditions is determined. This step can help evaluate the internal resistance management capability and stability of the system under different environments. The node internal resistance failure limit data is verified by cross-validation method to ensure the accuracy and reliability of the data. Through data segmentation verification, the applicability of the internal resistance failure limit model on different data sets is verified to enhance the credibility of the calculation results. Cross-validation can help reduce prediction errors caused by sample bias or model overfitting, and improve the accuracy of the calculation of the internal resistance failure probability. According to the internal resistance failure limit segmentation validation data, the law of large numbers is applied to the final calculation of the internal resistance failure probability. The probability of internal resistance failure under different conditions is calculated to provide a basis for the formulation of the system internal resistance management strategy. Based on the calculation results, the prevention and maintenance strategies of internal resistance management are optimized to extend the service life of the system and improve stability.

[0037] Preferably, step S24 includes the following steps:

[0038] Step S241: Calculating the thermal damage increment amplitudes of different nodes on the thermal damage gain effect quantification data to obtain thermal damage increment amplitude data;

[0039] Step S242: analyzing the mechanical stress variation between nodes according to the thermal damage increment amplitude data to obtain the node mechanical stress variation data;

[0040] Step S243: performing temperature-stress coupling effect analysis according to the thermal damage increment amplitude data and the node mechanical stress change data to obtain temperature-stress coupling effect data;

[0041] Step S244: performing node life degradation effect analysis based on the temperature-stress coupling effect data to obtain node life degradation effect data;

[0042] Step S245: Calculate the performance attenuation gradient based on the temperature-stress coupling effect data and the node life degradation effect data to obtain thermal damage performance attenuation gradient data.

[0043] The present invention uses data to quantify the incremental amplitude of thermal damage at different nodes. These data are based on actual or simulated thermal damage data and reflect the degree of thermal damage that the node may face during operation. Based on the incremental amplitude data of thermal damage, the changes in mechanical stress between different nodes are analyzed. This includes taking into account factors such as thermal expansion or structural deformation to evaluate the potential impact of thermal damage on the structural strength of the node. Combine the incremental amplitude data of thermal damage and the data on the change in mechanical stress of the node to analyze the complex interaction between temperature and mechanical stress. Determine how temperature changes lead to an increase or decrease in internal stress, thereby affecting the stability and performance of the node. Based on the temperature-stress coupling effect data, evaluate the life degradation of the node during long-term use. This includes taking into account factors such as material fatigue, structural deformation, and performance attenuation. Combine the temperature-stress coupling effect data and the node life degradation effect data to calculate the attenuation gradient of node performance over time. Based on the calculation results, predict the long-term stability and performance retention of the system under different operating conditions.

[0044] Preferably, step S3 comprises the following steps:

[0045] Step S31: performing intervention state identification according to the thermal damage performance attenuation gradient data and the internal resistance change series gradient influence data to obtain battery cell intervention state identification data;

[0046] Step S32: Formulate a battery cell balancing intervention strategy based on the battery cell intervention state identification data, the thermal damage performance attenuation gradient data, and the internal resistance change series gradient influence data to obtain a battery cell balancing intervention strategy.

[0047] The present invention identifies the current intervention state of the battery cell by analyzing the thermal damage performance attenuation gradient data and the internal resistance change series gradient influence data. The identification results are output, and these results reflect the health state and performance attenuation degree of the battery cell under specific operating conditions. According to the intervention state identification data of the battery cell, the thermal damage performance attenuation gradient data and the internal resistance change series gradient influence data, an appropriate balancing intervention strategy is formulated. Ensure that the charge and discharge of each battery cell in the series system are balanced to improve the overall performance and life of the battery pack. By analyzing the thermal damage performance attenuation gradient and the internal resistance change data, the battery cell state is accurately identified, so that the health problems and performance degradation trends of the battery cell are discovered in time. A balancing strategy is formulated based on the identified battery cell state data to ensure that each battery cell can be charged and discharged evenly, thereby extending the life and stability of the entire battery system. By implementing accurate intervention state identification and balancing strategies, the performance loss and safety risks that may be caused by unbalanced battery cells in the battery system are reduced, and the reliability and energy utilization efficiency of the system are improved.

[0048] Preferably, step S31 includes the following steps:

[0049] Step S311: Calculate the node thermal damage depths between different nodes according to the thermal damage performance attenuation gradient data and the internal resistance change series gradient influence data to obtain the node thermal damage depth data;

[0050] Step S312: evaluating the impact weight of the series node line according to the node thermal damage depth data and the series gradient impact data of the internal resistance change, and obtaining the node line impact weight evaluation data;

[0051] Step S313: calculating the bearing range of the series structure between different nodes according to the node thermal damage depth data and the series gradient influence data of the internal resistance change to obtain the bearing range of the series structure;

[0052] Step S314: using a preset node limit state identification model to perform intervention state identification on the node line influence weight assessment data and the series structure load-bearing interval to obtain battery cell intervention state identification data.

[0053] The present invention uses thermal damage performance attenuation gradient data and internal resistance change series gradient influence data to calculate the thermal damage depth between different nodes. These data reflect the degree of thermal damage of each node (such as a battery cell) in a battery system under specific conditions. Output node thermal damage depth data, which is crucial for the evaluation of the impact weight of the series node line and the calculation of the bearing range of the series structure in the subsequent steps. According to the node thermal damage depth data and the internal resistance change series gradient influence data, the impact weight of the series node line is evaluated. This evaluation helps determine the importance and influence of each node in the series system. Generate node line impact weight evaluation data for subsequent intervention state identification and battery cell balancing intervention strategy formulation. Calculate the series structure bearing range between different nodes using node thermal damage depth data and internal resistance change series gradient influence data. These ranges define the carrying capacity and safety boundary of the battery system under the current working state. Output series structure bearing range data for evaluating the current working state and safety performance of the battery system. Based on the preset limit state identification model, the node line impact weight evaluation data and the series structure bearing range are comprehensively analyzed and evaluated. Output the intervention state identification data of the battery cell, which indicates the current health state of the battery cell and the intervention requirements under the operating conditions.

[0054] Preferably, step S32 includes the following steps:

[0055] Step S321: identifying high-risk areas according to the battery cell intervention state identification data, the thermal damage performance attenuation gradient data, and the internal resistance change series gradient influence data, and obtaining series node high-risk area data;

[0056] Step S322: using the SARSA algorithm to perform environmental state perception on the high-risk area data of the series nodes to obtain high-risk environmental state perception data;

[0057] Step S323: adjusting the node power output according to the high-risk environment state perception data on the thermal damage performance attenuation gradient data and the internal resistance change series gradient impact data to obtain node power output adjustment data;

[0058] Step S324: formulating a battery cell balancing intervention strategy based on the high-risk environment state perception data and the node power consumption output adjustment data to obtain a battery cell balancing intervention strategy.

[0059] The present invention combines the battery cell intervention state identification data, the thermal damage performance attenuation gradient data and the internal resistance change series gradient influence data, and the system can accurately identify the potential high-risk series node areas in the battery system. These areas face performance degradation or safety risks due to thermal damage or internal resistance changes. Output specific high-risk area data to provide a basis for subsequent environmental state perception and adjustment. The SARSA algorithm is used to perceive the environmental state of the high-risk area data of the series node. This means that the system not only detects high-risk areas, but also can evaluate the impact of the current environment on these areas in real time. Output high-risk environmental state perception data, which reflects the risk level and response requirements of the battery system under different working environment conditions. According to the high-risk environmental state perception data, the system adjusts the node power consumption output in the thermal damage performance attenuation gradient data and the internal resistance change series gradient influence data. This adjustment involves reducing or increasing the load of a specific node to reduce further deterioration of the high-risk area. Output the adjusted node power consumption output data, which provides a more sophisticated management means for the stability and safety of the battery system. Utilize the high-risk environmental state perception data and the node power consumption output adjustment data to formulate an adaptable battery cell balancing intervention strategy. These strategies are designed to balance and optimize the system by adjusting the power consumption distribution between battery cells under different environmental conditions. Specific battery cell balancing intervention strategies are output, which help prevent further deterioration of high-risk areas and extend the life and stability of the battery system.

[0060] Preferably, the present invention further provides a balancing protection system for multiple battery cells connected in series, which is used to execute the balancing protection method for multiple battery cells connected in series as described above. The balancing protection system for multiple battery cells connected in series comprises:

[0061] A series gradient influence analysis module is used to collect multi-node battery operation status data of multiple series-connected battery cells to obtain multi-node battery operation status data; perform series gradient influence analysis on the multi-node battery operation status data to obtain series gradient influence data of internal resistance change;

[0062] The performance attenuation gradient calculation module is used to simulate the thermal damage gain effect according to the series gradient influence data of the internal resistance change to obtain the thermal damage gain effect data; the performance attenuation gradient is calculated according to the thermal damage gain effect data to obtain the thermal damage performance attenuation gradient data;

[0063] A balancing intervention strategy formulation module is used to formulate a balancing intervention strategy for the series battery cells according to the thermal damage performance attenuation gradient data to obtain a balancing intervention strategy for the battery cells;

[0064] The intervention strategy execution module is used to perform automatic firmware design based on the battery cell balancing intervention strategy to obtain the balancing intervention strategy firmware; and embed the balancing intervention strategy firmware into the battery cell management system to execute the balancing protection of the battery cell.

[0065] The beneficial effect of the present invention is that the operating status data of multiple series-connected battery cells are collected in real time, including parameters such as voltage, current, and temperature, to obtain a comprehensive battery working state, analyze the collected data, and pay special attention to the change of internal resistance. Internal resistance is an important indicator of battery health status, and its change can affect the performance and life of the battery. Detecting the change of internal resistance gradient can help to find potential problems or signs of failure in the battery early. According to the internal resistance gradient data, the remaining life of the battery can be more accurately evaluated to avoid unexpected shutdown or performance degradation caused by unexpected internal resistance changes. Thermal damage is one of the important reasons for battery performance degradation. Simulation analysis can help predict the change of internal temperature of the battery under different working conditions and its specific impact on performance. According to the simulation results, the working parameters or working strategies of the battery can be adjusted to minimize the negative impact of thermal damage on battery performance, determine the decay rate of battery performance under different conditions, and provide a basis for formulating appropriate maintenance plans and periodic inspections. Through periodic performance decay gradient calculation, the management strategy of the battery system can be continuously optimized to extend its reliable working time and life. According to the real-time monitoring data, the balancing operation of the battery core is adjusted to ensure that the charge state and voltage level between each battery cell remain balanced. According to different working conditions and ambient temperatures, the balancing intervention strategy is optimized to maximize the energy storage efficiency and power output stability of the battery system. Through precise balancing intervention strategies, the imbalance caused by excessive voltage differences between battery cells can be avoided. The balancing operation can reduce overcharging or overdischarging of the battery cells and reduce the risk of damage to the battery system. Effective balancing management can extend the service life of the battery and reduce the performance degradation and early replacement costs caused by battery imbalance. An algorithm that can automatically adjust the state of the battery cell according to the preset balancing strategy is developed, and the designed firmware is verified and optimized for security to ensure that the firmware can operate reliably under various working conditions. The working state of the battery cell can be monitored in real time, and the operation can be automatically adjusted according to the preset balancing strategy to maintain the stability and long-term performance of the battery system. The automated firmware design reduces the reliance on manual intervention and improves the autonomy and response speed of the management system. Therefore, the present invention provides a balancing protection method for multiple battery cells connected in series, which is an optimization of a traditional balancing protection method for multiple battery cells connected in series, and solves the problems of inaccurate performance attenuation analysis of the battery cells connected in series and inaccurate evaluation of heat loss effects in the traditional balancing protection method for multiple battery cells connected in series, and improves the accuracy of the performance attenuation analysis of the battery cells connected in series and the accuracy of the evaluation of the heat loss effects. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 A schematic flow chart of the steps of a method for balancing and protecting multiple battery cells connected in series;

[0067] Figure 2 for Figure 1 Detailed implementation steps of step S2 in FIG.

[0068] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0069] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0070] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0071] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0072] To achieve this, please refer to Figure 1 to Figure 2 , a method for balancing and protecting multiple battery cells connected in series, the method comprising the following steps:

[0073] Step S1: collecting multi-node battery operation status data of multiple series-connected battery cells to obtain multi-node battery operation status data; performing series gradient influence analysis on the multi-node battery operation status data to obtain internal resistance change series gradient influence data;

[0074] Step S2: simulating the thermal damage gain effect according to the series gradient influence data of the internal resistance change to obtain the thermal damage gain effect data; calculating the performance attenuation gradient according to the thermal damage gain effect data to obtain the thermal damage performance attenuation gradient data;

[0075] Step S3: formulating a battery cell balancing intervention strategy based on the thermal damage performance attenuation gradient data to obtain a battery cell balancing intervention strategy;

[0076] Step S4: performing automated firmware design based on the battery cell balancing intervention strategy to obtain balancing intervention strategy firmware; embedding the balancing intervention strategy firmware into the battery cell management system to perform balancing protection of the battery cells.

[0077] In the embodiment of the present invention, reference Figure 1 The above is a schematic flow chart of the steps of a method for balancing and protecting multiple battery cells connected in series according to the present invention. In this example, the method for balancing and protecting multiple battery cells connected in series includes the following steps:

[0078] Step S1: collecting multi-node battery operation status data of multiple series-connected battery cells to obtain multi-node battery operation status data; performing series gradient influence analysis on the multi-node battery operation status data to obtain internal resistance change series gradient influence data;

[0079] In the embodiment of the present invention, voltage, current, temperature and internal resistance sensors are installed on each battery cell to ensure that these parameters can be monitored in real time. The operating status data of each node (battery cell) is collected through a data acquisition system. The data acquisition frequency can be determined according to actual needs (for example, once per second) to ensure that the data acquisition system can synchronously collect data from each node to avoid the impact of time errors. The internal resistance of each battery cell is measured and the change of internal resistance at different time points is recorded. The internal resistance change data is analyzed using statistical analysis tools or data processing software (such as Matlab or Python), the gradient of the internal resistance change (i.e., the difference in internal resistance change between different nodes) is calculated, and the gradient distribution of the internal resistance change is visualized to determine which battery cells in series have a greater impact on the overall internal resistance.

[0080] Step S2: simulating the thermal damage gain effect according to the series gradient influence data of the internal resistance change to obtain the thermal damage gain effect data; calculating the performance attenuation gradient according to the thermal damage gain effect data to obtain the thermal damage performance attenuation gradient data;

[0081] In an embodiment of the present invention, a thermal model is constructed to simulate the thermal behavior of the battery cell. Finite element analysis (FEA) software such as COMSOL or Ansys can be used, or the numerical solution of the heat equation can be used to simulate the heat distribution. The internal resistance change gradient data is input into the thermal model to simulate the heat generation of the battery pack during use under different current conditions. Considering the physical structure and heat dissipation conditions of the battery pack, the temperature distribution and change of each node are calculated. The temperature rise of each node is analyzed to determine which nodes have higher thermal damage. According to the thermal damage gain effect data, the life attenuation of each node is evaluated. Empirical formulas or experimental data can be used to quantify the effect of temperature on battery life. The performance attenuation rate of each node is calculated, and a performance attenuation gradient model is constructed to reflect the asynchronous changes in the performance attenuation of each node. The model is used to predict the overall performance attenuation trend of the battery pack under different usage conditions (for example, different discharge rates and charging rates).

[0082] Step S3: formulating a battery cell balancing intervention strategy based on the thermal damage performance attenuation gradient data to obtain a battery cell balancing intervention strategy;

[0083] In an embodiment of the present invention, the thermal damage performance attenuation gradient data is used to identify battery cells with faster performance attenuation and which cells need more frequent balancing. The performance attenuation gradient data is clustered using statistical analysis tools to identify groups of battery cells with similar attenuation characteristics. Based on the data analysis results, the target of balancing is defined. For example, the state of each battery cell is adjusted by active balancing or passive balancing so that the entire battery pack maintains a uniform voltage and temperature distribution during charging and discharging. The trigger conditions and thresholds for balancing are determined, for example, when the voltage difference of the battery cells exceeds a certain value or the temperature of some battery cells exceeds a set safety threshold, the balancing operation is started. The voltage of the battery cells is balanced by consuming excess energy (such as discharging through a resistor). Energy is transferred from a cell with a higher voltage to a cell with a lower voltage through energy transfer (such as using an inductor, a transformer, or a DC-DC converter). The time interval for the balancing operation is defined, such as performing the balancing operation during charging, discharging, or in standby. The frequency of the balancing operation is determined depending on the usage and balancing requirements of the battery pack. An algorithm for controlling the balancing operation is developed, such as a method based on PID control, fuzzy logic control, or machine learning, to dynamically adjust the balancing operation. Use simulation tools to simulate the balancing strategy and evaluate its performance under different working conditions. Perform prototype tests in a laboratory environment and verify the effectiveness and stability of the balancing strategy through actual battery pack tests. Monitor the changes in battery cell voltage, temperature, and internal resistance during the test to evaluate the impact of balancing operations on battery pack performance.

[0084] Step S4: performing automated firmware design based on the battery cell balancing intervention strategy to obtain balancing intervention strategy firmware; embedding the balancing intervention strategy firmware into the battery cell management system to perform balancing protection of the battery cells.

[0085] In an embodiment of the present invention, according to the equalization intervention strategy, the functions that the firmware needs to implement are defined, such as battery cell monitoring, equalization control signal generation, and data communication. The hardware interface requirements of the firmware are determined, such as the interface for communicating with the battery management system (BMS), the input interface of the sensor signal, and the output interface of the equalization controller. The overall architecture of the firmware is designed, including control logic, data processing, and communication modules. The real-time requirements of the firmware are determined to ensure that the equalization operation can respond to changes in the battery state in a timely manner. The control algorithm in the equalization strategy is encoded as a control logic module in the firmware to ensure that the algorithm can run efficiently in the firmware environment. Consider the optimization of the algorithm to reduce the computational burden and energy consumption of the firmware. Use embedded programming languages ​​(such as C or C++) to develop firmware to implement the equalization control function. Develop drivers for communicating with BMS and external systems to ensure that the firmware can work in conjunction with other system modules. Perform unit tests and integration tests to ensure the correctness and stability of the firmware functions. Use hardware-in-the-loop (HIL) test tools to simulate the operating status of the battery cell and verify the performance of the firmware under various operating conditions. Optimize firmware performance based on test results to reduce latency and power consumption and improve response speed. Ensure that the firmware can efficiently manage the balancing operation of the battery cells and maximize the life of the battery pack. Load the developed firmware into the microcontroller of the BMS and ensure that it can start and run correctly. Verify the compatibility of the firmware with the BMS and battery pack hardware to ensure that the balancing operation can be seamlessly integrated into the battery management system. Deploy the firmware to the actual battery management system for field testing and verification. Ensure that each step in the deployment process is rigorously tested and verified to prevent potential problems. Monitor the operating status of the firmware in real time, collect the operating data of the battery cells, and evaluate the actual effect of the balancing strategy. Establish an early warning system to detect any abnormal conditions and intervene and correct them in time. Based on the monitoring data and feedback, continuously optimize and improve the firmware and balancing strategy to adapt to the actual operation of the battery pack. Update the firmware regularly to respond to new requirements or correct discovered problems.

[0086] Preferably, step S1 comprises the following steps:

[0087] Step S11: using distributed sensors to collect multi-node battery operation status data of multiple series-connected battery cells to obtain multi-node battery operation status data;

[0088] Step S12: performing data cleaning on the multi-node battery operation status data to obtain multi-node battery operation status cleaning data;

[0089] Step S13: mapping the internal resistance change according to the multi-node battery operation status cleaning data to obtain multi-node internal resistance change mapping data;

[0090] Step S14: performing series gradient influence analysis on the multi-node internal resistance change mapping data to obtain internal resistance change series gradient influence data.

[0091] In an embodiment of the present invention, sensors are installed on each battery cell, including a voltage sensor, a current sensor, a temperature sensor, and an internal resistance measuring device. Use high-precision and fast-response sensors to ensure that the collected data is accurate and timely. Ensure that the sensors are evenly arranged and can cover the entire battery pack, and the physical structure and heat dissipation conditions of the battery should be taken into consideration during installation. Select suitable data acquisition devices (such as microcontrollers, data loggers) that can handle multi-sensor inputs and support high-frequency data acquisition. Set the data acquisition frequency, which is generally recommended to be between 1Hz and 10Hz, depending on the dynamic requirements of the application scenario. Ensure that the data acquisition system can stably transmit data from the sensor network to the central data storage or processing unit through a wireless or wired network. Start the data acquisition system and start collecting the voltage, current, temperature, and internal resistance data of the battery cell. Use data monitoring software to display and record the operating status of each battery cell in real time for subsequent analysis. Set up alarm and notification mechanisms to monitor data anomalies, such as excessive temperature, voltage imbalance, etc., and take timely measures. Check and remove incomplete or damaged data records, such as missing values ​​caused by sensor failure or communication interruption. Perform range checks on the data to remove or mark values ​​that are outside of a reasonable range (e.g., extreme values ​​of voltage or temperature). Process and replace sensor noise and erroneous readings, such as using a moving average or median filter to smooth the data. Calculate the instantaneous internal resistance of each battery cell using Ohm's law based on the cleaned voltage and current data. Record the change in internal resistance at different time points (e.g., during the charge and discharge cycle) to generate time series internal resistance data. Use statistical methods to calculate the change trend of internal resistance, such as using a sliding window to calculate the average internal resistance change rate. Draw a time series graph of internal resistance change to show how the internal resistance of each battery cell changes over time. Use heat maps or 3D charts to show the distribution of internal resistance of the entire battery pack at different time points. Generate contour maps of internal resistance change to show the gradient of internal resistance change at different nodes. Use clustering algorithms (e.g., K-means or hierarchical clustering) to perform pattern recognition on internal resistance change data to find battery cells with similar internal resistance change characteristics. Identify periodicity and trends in internal resistance changes and analyze their relationship with the operating status of the battery pack (e.g., charging, discharging, temperature). Identify abnormal points in the internal resistance change through anomaly detection algorithms, indicating possible faults or performance issues. Calculate the relative change rate of the internal resistance of each battery cell and analyze their positional relationship in the battery pack. Use the differential method to calculate the internal resistance difference between adjacent nodes to obtain the internal resistance gradient within the battery pack. Generate a distribution map of the internal resistance gradient to show the distribution of the internal resistance change in the battery pack. Use electrochemical models or simulation tools to simulate the impact of the internal resistance gradient on battery pack performance, such as voltage imbalance and uneven heat distribution during discharge. Analyze the impact of the internal resistance gradient on battery pack efficiency, thermal management, and life, and evaluate possible performance bottlenecks and optimization space.Combined with actual usage conditions and test data, the results of the internal resistance gradient analysis are verified and the analysis model is adjusted. Based on the internal resistance gradient analysis, a balanced control strategy is proposed, such as how to balance the internal resistance change by adjusting the charge and discharge current. It is recommended to improve the thermal management solution of the battery pack to reduce the adverse effects of the internal resistance gradient on the heat distribution. It is recommended to improve the manufacturing and assembly process of the battery pack to reduce the generation and impact of the internal resistance gradient.

[0092] Preferably, step S2 comprises the following steps:

[0093] Step S21: performing multi-node temperature change matching according to the internal resistance change series gradient influence data to obtain multi-node temperature change data;

[0094] Step S22: simulating the thermal damage gain effect according to the internal resistance change series gradient influence data and the multi-node temperature change data to obtain the thermal damage gain effect data;

[0095] Step S23: performing gain effect quantification processing on the thermal damage gain effect data to obtain thermal damage gain effect quantification data;

[0096] Step S24: Calculate the performance attenuation gradient based on the quantified data of the thermal damage gain effect to obtain thermal damage performance attenuation gradient data.

[0097] As an example of the present invention, refer to Figure 2 As shown, in this example, step S2 includes:

[0098] Step S21: performing multi-node temperature change matching according to the internal resistance change series gradient influence data to obtain multi-node temperature change data;

[0099] In the embodiment of the present invention, a high-precision temperature sensor is used to monitor the temperature change in real time on each battery cell. Ensure that the data acquisition frequency and synchronization of the temperature sensor are consistent with the internal resistance change data. Synchronize the temperature data with the previously collected voltage, current and internal resistance data and record them in the same time series. Align the internal resistance change data and the temperature data through the timestamp to ensure that the two data sets are synchronized in the time dimension. For asynchronous data, the interpolation method is used to align the time points so that each time point has corresponding internal resistance and temperature data. To deal with the problem of mismatched acquisition frequency, it is necessary to downsample the high-frequency data or interpolate the low-frequency data. Calculate the internal resistance change rate and temperature change rate of each battery cell, and use statistical methods to analyze the correlation between them. Visualize the matching relationship between the internal resistance change and the temperature change, draw a scatter plot or a time series plot, and show the linkage between the internal resistance and temperature changes of each node. Classify and analyze the internal resistance and temperature changes under different time periods or different working conditions (such as charging, discharging, and standby) to find the matching mode under specific conditions. Use clustering or pattern recognition algorithms to classify temperature data and identify battery cells with similar temperature change characteristics. Analyze periodicity and trends in temperature changes to identify high temperature areas or time periods that may cause thermal damage. Combine temperature change data with matching patterns in internal resistance change data to identify heat-sensitive battery cells.

[0100] Step S22: simulating the thermal damage gain effect according to the internal resistance change series gradient influence data and the multi-node temperature change data to obtain the thermal damage gain effect data;

[0101] In an embodiment of the present invention, a thermal model of a battery cell is established based on the internal resistance change and temperature change data. Finite element analysis (FEA) software such as COMSOL, Ansys, or simplified thermodynamic equations can be used. The model needs to consider the thermal capacity, thermal conductivity, heat dissipation conditions, and internal heating mechanism of the battery cell. Determine the boundary conditions and initial conditions of the model, including ambient temperature and heat dissipation coefficient, input the internal resistance change data of each battery cell in the thermal model, and calculate its heat generation. Use the heat conduction equation to simulate the heat transfer between battery cells, taking into account the arrangement of the battery cells and the heat dissipation path. Through simulation, the temperature distribution and change of the battery pack under different working conditions are obtained, and hot spots are identified. In the thermal model, the temperature change of the battery cell at different time points and operating conditions is simulated, and the cumulative thermal damage is calculated. Use thermal damage theory (such as the Arrhenius equation) to quantify the effect of temperature on battery material aging and performance degradation. Combined with the internal resistance change data, evaluate the gain effect of temperature change on the increase of the internal resistance of the battery cell, and analyze the coupling effect of internal resistance and temperature. Draw a temperature distribution diagram of the battery pack to show the temperature change at different time points and operating conditions. Use heat maps or 3D charts to show the distribution of thermal damage and identify high-risk areas prone to thermal damage. Plot the relationship between internal resistance increase and temperature change to show the impact of thermal damage gain effect. Perform temperature tests and internal resistance change tests on battery packs in a laboratory environment and collect actual operating data. Compare experimental data with simulation results to evaluate the accuracy and credibility of the model. Based on actual data, calibrate and optimize the thermal damage model to ensure that it can accurately reflect the thermal damage gain effect of the battery pack.

[0102] Step S23: performing gain effect quantification processing on the thermal damage gain effect data to obtain thermal damage gain effect quantification data;

[0103] In an embodiment of the present invention, the thermal damage data obtained by simulation is collected and sorted, including the temperature change, internal resistance change, and cumulative thermal damage of each battery cell. The data is classified and summarized according to time and operating conditions to form a thermal damage effect data set under different scenarios. A large amount of simulation data is preliminarily processed using data sorting tools (such as Excel, Python Pandas) to ensure data integrity and consistency. Thermal damage indicators such as maximum temperature rise, average temperature rise, internal resistance growth rate, and cumulative thermal damage are selected, and normalization methods (such as minimum-maximum normalization or Z-score normalization) are used to process temperature, internal resistance and other data to ensure that data of different dimensions can be compared on the same scale. During the processing, a suitable data range and standardization method are selected to maintain the physical meaning and relative relationship of the data, and statistical methods (such as mean, standard deviation, median, quantile, etc.) are used to analyze the basic distribution of the thermal damage gain effect quantitative data. Draw a histogram, box plot or probability density plot of the data to show the distribution characteristics and outliers of the thermal damage gain effect. Identify the changing rules of the thermal damage gain effect under different operating conditions, such as the trend of increasing thermal damage under high load conditions. Use clustering algorithms (such as K-means, hierarchical clustering) to group quantitative data and identify battery cells with similar thermal damage characteristics. Based on the clustering results, define different thermal damage risk levels to facilitate subsequent management and protection measures. Conduct in-depth analysis of different categories of data to identify the key factors and mechanisms that affect the thermal damage gain effect.

[0104] Step S24: Calculate the performance attenuation gradient based on the quantified data of the thermal damage gain effect to obtain thermal damage performance attenuation gradient data.

[0105] In an embodiment of the present invention, a performance attenuation model is established based on the physical properties of the battery and the thermal damage effect. Common models include the Arrhenius model, the Peukert law, and the empirical regression model. The model should be able to consider the effects of temperature, internal resistance changes, and usage conditions on battery performance (such as capacity, efficiency, and life). The model is trained and verified using historical data to ensure that it can accurately predict the performance attenuation of the battery. The quantified thermal damage gain effect data is input into the performance attenuation model to calculate the performance attenuation of each battery cell. Quantify the indicators of performance attenuation, such as capacity retention rate, internal resistance growth rate, and charge and discharge efficiency decline. Analyze the degree of influence of the thermal damage gain effect on different performance indicators and find out the main attenuation factors. Use the differential method to calculate the performance attenuation difference between adjacent battery cells to obtain the performance attenuation gradient. Draw a distribution map of the performance attenuation gradient to show the performance attenuation distribution of the entire battery pack. Use statistical and graphical tools (such as heat maps and gradient maps) to display the performance attenuation levels and gradient changes of different battery cells. According to the performance attenuation gradient, the risk level of the high performance attenuation area in the battery pack is evaluated. Analyze the causes of the high attenuation gradient area, such as whether it is due to thermal damage, poor usage conditions, or manufacturing defects. Improvement measures are proposed, such as strengthening thermal management, optimizing usage conditions, and adjusting charging and discharging strategies, to reduce performance degradation in high-risk areas.

[0106] Preferably, step S22 includes the following steps:

[0107] Step S221: Calculating the heat capacity storage efficiency between different nodes according to the temperature change data of multiple nodes to obtain heat capacity storage efficiency data;

[0108] Step S222: Calculating the heat transfer coefficients between different nodes based on the heat capacity storage efficiency data to obtain the node heat transfer coefficients;

[0109] Step S223: performing node thermal sensitivity quantification processing according to the thermal capacity storage efficiency data and the node heat transfer coefficient to obtain node thermal sensitivity quantification data;

[0110] Step S224: evaluating the node thermal damage impact range degree according to the node thermal sensitivity quantified data and the node heat transfer coefficient, and obtaining the node thermal damage impact range data;

[0111] Step S225: calculating the internal resistance failure probability of the internal resistance change series gradient influence data according to the node thermal damage influence range data to obtain the internal resistance failure probability data;

[0112] Step S226: Perform a thermal damage gain effect simulation based on the node thermal damage influence range data and the internal resistance failure probability data to obtain thermal damage gain effect data.

[0113] The present invention measures the heat capacity of battery nodes under laboratory conditions. The heat absorbed or released during unit temperature change can be measured through heating and cooling experiments, and the temperature change of each battery node can be recorded using a high-precision thermocouple or infrared thermometer. The heat capacity storage efficiency of each battery node is calculated to quantify its heat storage capacity. Based on the actual measured data, the actual heat capacity of each node is calculated. The heat capacity storage efficiency is defined as the ratio of the actual heat capacity to the theoretical heat capacity. The theoretical heat capacity can be estimated based on the thermophysical parameters of the battery material and structure. The heat capacity storage efficiency data of each node are summarized and recorded to form a complete data set. The difference in heat capacity storage efficiency between different nodes is analyzed to find out efficient and inefficient nodes. Statistical analysis tools such as mean, standard deviation, distribution map, etc. are used to analyze the overall characteristics and distribution of heat capacity storage efficiency. The structure and layout of the battery pack are analyzed to determine the heat transfer path between battery nodes. Consider the three main heat transfer modes of conduction, convection and radiation, and identify their actual role in the battery pack. For closely arranged battery nodes, the main focus is on conduction heat flow; for cases with significant air flow, the convection effect is considered. In the experiment, a known temperature change is applied to the battery node, and the temperature response of the adjacent nodes is measured. The thermal conductivity coefficient between nodes is calculated using Fourier's law of heat conduction. For convection and radiation heat transfer, the corresponding convection heat transfer coefficient and radiation heat transfer formula are used for calculation. The heat transfer coefficient data between different nodes are organized into a matrix or table for subsequent analysis. Statistical analysis is performed on the heat transfer coefficient to find out the node pairs with high and low heat transfer. The relationship between the heat transfer coefficient and factors such as node spacing, materials, and surface conditions is analyzed to understand the heat transfer mechanism. Appropriate thermal sensitivity indicators are selected, such as temperature response speed, temperature change amplitude, and the dependence of internal resistance change on temperature. The thermal sensitivity quantitative value of each node is calculated using the defined thermal sensitivity indicators. Statistical analysis is performed on the thermal sensitivity data to find out the nodes with high and low sensitivity. Use charts (such as thermal sensitivity curves and heat maps) to show the distribution and changes of thermal sensitivity of different nodes. Use cluster analysis or classification algorithms to classify nodes into different thermal sensitivity levels. According to the thermal sensitivity level, the thermal response characteristics of different nodes in actual operation are evaluated. Analyze nodes with high thermal sensitivity and find possible improvement measures, such as strengthening heat dissipation or reducing load. Based on the thermal sensitivity and heat transfer coefficient of the node, define the thermal damage impact range as the distance and degree of heat propagation from one node to the adjacent node. The impact range can be calculated using the thermal damage impact radius to calculate the thermal damage impact range of each node. Summarize the impact range data to form a thermal damage impact network between nodes. Use heat maps or impact range maps to show the thermal damage impact range and distribution of each node. Analyze nodes with a large thermal damage impact range and assess their thermal damage risk to surrounding nodes. Propose optimization measures, such as strengthening heat dissipation, adding insulation materials, or adjusting battery arrangement to reduce the thermal damage impact range.Simulate and verify the effects of the optimization measures to ensure that they can effectively reduce the risk of thermal damage. Based on the thermal damage data, establish a relationship model between the change in internal resistance and thermal damage. Use statistical and failure analysis methods, such as Weibull distribution or Monte Carlo simulation, to establish a model for the probability of failure of internal resistance. Define the probability of failure of internal resistance as the probability that the internal resistance exceeds the failure threshold under specific thermal damage conditions. Use the established failure probability model to calculate the probability of failure of internal resistance at each node under different thermal damage conditions. Arrange the failure probability data into a table or matrix to show the failure risk of different nodes. Perform statistical analysis on the failure probability to identify nodes and areas with high failure risk. Analyze the nodes with high failure probability to find out the causes of high risk, such as high temperature, frequent use or manufacturing defects. Develop risk management strategies, such as optimizing usage conditions, regular maintenance and monitoring of high-risk nodes. Use fault tree analysis (FTA) or failure mode and effects analysis (FMEA) tools to further refine the risk assessment of internal resistance failure. Build a comprehensive thermal damage gain effect model based on the previous data on thermal capacity storage efficiency, heat transfer coefficient, thermal sensitivity and internal resistance failure probability. The model should be able to simulate how thermal damage propagates and accumulates between nodes under different operating conditions. Use finite element analysis (FEA) or numerical simulation methods to numerically solve and simulate the model. Use the simulation model to simulate different operating conditions and thermal damage scenarios. Record and analyze the thermal damage gain effect of each node, including temperature rise, internal resistance change, and failure probability. Compare the simulation results under different conditions to identify the nodes and areas most susceptible to thermal damage. Based on the simulation results, propose improvement measures to reduce the thermal damage gain effect, such as optimizing the heat dissipation design, adjusting the charging and discharging strategy, or improving the battery material. Re-simulate and verify the improvement measures to evaluate their impact on the thermal damage gain effect. Develop and implement optimized operation strategies and designs to ensure the long-term stability and safety of the battery system.

[0114] Preferably, calculating the internal resistance failure probability of the internal resistance change series gradient influence data comprises the following steps:

[0115] The influence range difference between different nodes is calculated for the node thermal damage influence range data to obtain the influence range difference data;

[0116] Perform thermal interaction internal resistance influence correction on the internal resistance change series gradient influence data according to the influence range difference data to obtain internal resistance change series correction data;

[0117] Calculate the node internal resistance failure limit based on the impact range difference data and the internal resistance change series correction data to obtain the node internal resistance failure limit data;

[0118] The cross-validation method is used to perform data cross-segmentation validation on the node internal resistance failure limit data to obtain the internal resistance failure limit segmentation validation data;

[0119] Based on the law of large numbers, the internal resistance failure probability is calculated for the internal resistance failure limit segmentation verification data to obtain the internal resistance failure probability data.

[0120] In an embodiment of the present invention, the node thermal damage influence range data is converted into influence range difference data. This involves calculating the influence range differences between different nodes. Mathematical methods or statistical analysis can be used to measure the thermal damage influence between nodes, and the influence range difference data is used to correct the thermal interaction internal resistance influence of the internal resistance change series gradient influence data. This step takes into account the thermal interaction effect between different nodes, and may adjust the original internal resistance change data to more accurately reflect the internal resistance change influence between nodes. Based on the influence range difference data and the corrected internal resistance change series correction data, the node internal resistance failure limit is calculated. This step usually involves model analysis of thermal damage and internal resistance change, determining the internal resistance limit of each node that theoretically fails, and verifying and segmenting the node internal resistance failure limit data using a cross-validation method. This includes dividing the data set into multiple subsets, training the model on one subset, and verifying it on another subset to evaluate the accuracy and generality of the model, and calculating the internal resistance failure probability of the node internal resistance failure limit segmentation verification data based on the law of large numbers and the results of cross-validation. This step aims to determine the probability of failure of the internal resistance of each node by statistical methods, taking into account the randomness and uncertainty factors in practical applications.

[0121] Preferably, step S24 includes the following steps:

[0122] Step S241: Calculating the thermal damage increment amplitudes of different nodes on the thermal damage gain effect quantification data to obtain thermal damage increment amplitude data;

[0123] Step S242: analyzing the mechanical stress variation between nodes according to the thermal damage increment amplitude data to obtain the node mechanical stress variation data;

[0124] Step S243: performing temperature-stress coupling effect analysis according to the thermal damage increment amplitude data and the node mechanical stress change data to obtain temperature-stress coupling effect data;

[0125] Step S244: performing node life degradation effect analysis based on the temperature-stress coupling effect data to obtain node life degradation effect data;

[0126] Step S245: Calculate the performance attenuation gradient based on the temperature-stress coupling effect data and the node life degradation effect data to obtain thermal damage performance attenuation gradient data.

[0127] In an embodiment of the present invention, the thermal damage gain effect data of different nodes are collected and quantified. The thermal damage increment amplitude of different nodes is calculated, which involves measuring or simulating the change in the degree of thermal damage of the nodes under different temperature conditions. According to the thermal damage increment amplitude data obtained in step S241, the mechanical stress change analysis between the nodes is performed. This analysis includes the use of tools such as mechanical models or finite element analysis to determine the impact of thermal damage on the mechanical stress of the nodes. Combining the thermal damage increment amplitude data and the node mechanical stress change data, the temperature stress coupling effect analysis is performed. This step considers the impact of temperature changes on the internal stress distribution and mechanical properties of the node, so as to evaluate the specific impact of the temperature stress coupling effect. Based on the temperature stress coupling effect data, the node life degradation effect is analyzed. This includes predicting the degree of life degradation of the node due to the temperature stress coupling effect under actual working conditions, involving the application of material science, electrochemical models or experimental data, and using the temperature stress coupling effect data and the node life degradation effect data to calculate the performance attenuation gradient. The goal of this step is to quantify the degree of influence of thermal damage on the node performance attenuation, which is usually accomplished through mathematical modeling or statistical analysis.

[0128] Preferably, step S3 comprises the following steps:

[0129] Step S31: performing intervention state identification according to the thermal damage performance attenuation gradient data and the internal resistance change series gradient influence data to obtain battery cell intervention state identification data;

[0130] Step S32: Formulate a battery cell balancing intervention strategy based on the battery cell intervention state identification data, the thermal damage performance attenuation gradient data, and the internal resistance change series gradient influence data to obtain a battery cell balancing intervention strategy.

[0131] In an embodiment of the present invention, thermal damage performance attenuation gradient data and internal resistance change series gradient influence data are used to identify the intervention state of the battery cell. This step involves using a machine learning algorithm or a rule engine to analyze data to identify the current operating state or health state of the battery cell. Based on the battery cell intervention state identification data, thermal damage performance attenuation gradient data, and internal resistance change series gradient influence data, a balanced intervention strategy for the series-connected battery cells is formulated. This involves adjusting the battery charging and discharging strategy, current distribution, voltage control, and other aspects to ensure balanced operation and service life of each battery cell under different conditions.

[0132] Preferably, step S31 includes the following steps:

[0133] Step S311: Calculate the node thermal damage depths between different nodes according to the thermal damage performance attenuation gradient data and the internal resistance change series gradient influence data to obtain the node thermal damage depth data;

[0134] Step S312: evaluating the impact weight of the series node line according to the node thermal damage depth data and the series gradient impact data of the internal resistance change, and obtaining the node line impact weight evaluation data;

[0135] Step S313: calculating the bearing range of the series structure between different nodes according to the node thermal damage depth data and the series gradient influence data of the internal resistance change to obtain the bearing range of the series structure;

[0136] Step S314: using a preset node limit state identification model to perform intervention state identification on the node line influence weight assessment data and the series structure load-bearing interval to obtain battery cell intervention state identification data.

[0137] In an embodiment of the present invention, thermal damage performance attenuation gradient data and internal resistance change series gradient influence data are collected, and the collected data are used to calculate the thermal damage depth between different nodes. This can be accomplished by mathematical modeling or simulation based on a physical model. Considering the degree of influence of temperature changes on node performance, the node thermal damage depth data and internal resistance change series gradient influence data obtained in step S311 are used to evaluate the line influence weights between different nodes. Node line influence weight evaluation data are output, which reflect the degree of mutual influence between different nodes in the battery system. According to the node thermal damage depth data and the internal resistance change series gradient influence data, the series structure load interval between different nodes is calculated. The calculated series structure load interval data is output, which is used to evaluate the reliability and safety of the series structure under different working conditions. Using a preset node limit state identification model, combined with the node line influence weight evaluation data of step S312 and the series structure load interval data of step S313, the battery cell intervention state identification is performed. Battery cell intervention state identification data are output, which reflect the health status and possible risks of each battery cell under the current working state.

[0138] Preferably, step S32 includes the following steps:

[0139] Step S321: identifying high-risk areas according to the battery cell intervention state identification data, the thermal damage performance attenuation gradient data, and the internal resistance change series gradient influence data, and obtaining series node high-risk area data;

[0140] Step S322: using the SARSA algorithm to perform environmental state perception on the high-risk area data of the series nodes to obtain high-risk environmental state perception data;

[0141] Step S323: adjusting the node power output according to the high-risk environment state perception data on the thermal damage performance attenuation gradient data and the internal resistance change series gradient impact data to obtain node power output adjustment data;

[0142] Step S324: formulating a battery cell balancing intervention strategy based on the high-risk environment state perception data and the node power consumption output adjustment data to obtain a battery cell balancing intervention strategy.

[0143] The present invention uses the battery cell intervention state identification data obtained in step S314 to prepare the thermal damage performance attenuation gradient data and the internal resistance change series gradient influence data obtained in step S31, and identifies the high-risk area in the series node based on the above data. This includes areas where the battery cell has safety, health or performance problems under specific working conditions. The SARSA (State-Action-Reward-State-Action) algorithm is used to perceive the environmental state of the high-risk area data of the series node. The perceived high-risk environmental state data is output, which reflects the safety and stability risks of the battery system under different operating environments. According to the high-risk environmental state perception data, the thermal damage performance attenuation gradient data and the internal resistance change series gradient influence data are adjusted for node power consumption output. The adjusted node power consumption output data is output to optimize the operating stability and safety of the battery system under high-risk environments. An adaptive battery cell balancing intervention strategy is formulated. The battery cell balancing intervention strategy is output, which includes current distribution, charge and discharge control strategies, etc., to ensure the stability and performance of the battery system under different operating environments.

[0144] Preferably, the present invention further provides a balancing protection system for multiple battery cells connected in series, which is used to execute the balancing protection method for multiple battery cells connected in series as described above. The balancing protection system for multiple battery cells connected in series comprises:

[0145] A series gradient influence analysis module is used to collect multi-node battery operation status data of multiple series-connected battery cells to obtain multi-node battery operation status data; perform series gradient influence analysis on the multi-node battery operation status data to obtain series gradient influence data of internal resistance change;

[0146] The performance attenuation gradient calculation module is used to simulate the thermal damage gain effect according to the series gradient influence data of the internal resistance change to obtain the thermal damage gain effect data; the performance attenuation gradient is calculated according to the thermal damage gain effect data to obtain the thermal damage performance attenuation gradient data;

[0147] A balancing intervention strategy formulation module is used to formulate a balancing intervention strategy for the series battery cells according to the thermal damage performance attenuation gradient data to obtain a balancing intervention strategy for the battery cells;

[0148] The intervention strategy execution module is used to perform automatic firmware design based on the battery cell balancing intervention strategy to obtain the balancing intervention strategy firmware; and embed the balancing intervention strategy firmware into the battery cell management system to execute the balancing protection of the battery cell.

[0149] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0150] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A method for balancing and protecting multiple battery cells in series, characterized in that: The following steps are involved: Step S1: collecting multi-node battery operation status data of multiple series-connected battery cells to obtain multi-node battery operation status data; Performing series gradient influence analysis on multi-node battery operation status data to obtain internal resistance change series gradient influence data; wherein step S1 includes: Step S11: using distributed sensors to collect multi-node battery operation status data of multiple series-connected battery cells to obtain multi-node battery operation status data; Step S12: performing data cleaning on the multi-node battery operation status data to obtain multi-node battery operation status cleaning data; Step S13: mapping the internal resistance change according to the multi-node battery operation status cleaning data to obtain multi-node internal resistance change mapping data; Step S14: performing series gradient influence analysis on the multi-node internal resistance change mapping data to obtain internal resistance change series gradient influence data; Step S2: simulating the thermal damage gain effect according to the internal resistance change series gradient influence data to obtain thermal damage gain effect data; calculating the performance attenuation gradient according to the thermal damage gain effect data to obtain thermal damage performance attenuation gradient data; wherein step S2 includes: Step S21: performing multi-node temperature change matching according to the internal resistance change series gradient influence data to obtain multi-node temperature change data; Step S22: simulating the thermal damage gain effect according to the internal resistance change series gradient influence data and the multi-node temperature change data to obtain thermal damage gain effect data; wherein step S22 includes: Step S221: Calculating the heat capacity storage efficiency between different nodes according to the temperature change data of multiple nodes to obtain heat capacity storage efficiency data; Step S222: Calculating the heat transfer coefficients between different nodes based on the heat capacity storage efficiency data to obtain the node heat transfer coefficients; Step S223: performing node thermal sensitivity quantification processing according to the thermal capacity storage efficiency data and the node heat transfer coefficient to obtain node thermal sensitivity quantification data; Step S224: evaluating the node thermal damage impact range degree according to the node thermal sensitivity quantified data and the node heat transfer coefficient, and obtaining the node thermal damage impact range data; Step S225: calculating the internal resistance failure probability of the internal resistance change series gradient influence data according to the node thermal damage influence range data to obtain the internal resistance failure probability data; Step S226: simulating the thermal damage gain effect according to the node thermal damage influence range data and the internal resistance failure probability data to obtain thermal damage gain effect data; Step S23: performing gain effect quantification processing on the thermal damage gain effect data to obtain thermal damage gain effect quantification data; Step S24: Calculate the performance attenuation gradient according to the quantified data of the thermal damage gain effect to obtain thermal damage performance attenuation gradient data; wherein step S24 includes: Step S241: Calculating the thermal damage increment amplitudes of different nodes on the thermal damage gain effect quantification data to obtain thermal damage increment amplitude data; Step S242: analyzing the mechanical stress variation between nodes according to the thermal damage increment amplitude data to obtain the node mechanical stress variation data; Step S243: performing temperature-stress coupling effect analysis according to the thermal damage increment amplitude data and the node mechanical stress change data to obtain temperature-stress coupling effect data; Step S244: performing node life degradation effect analysis based on the temperature-stress coupling effect data to obtain node life degradation effect data; Step S245: Calculate the performance attenuation gradient according to the temperature-stress coupling effect data and the node life degradation effect data to obtain the thermal damage performance attenuation gradient data; Step S3: formulating a battery cell balancing intervention strategy based on the thermal damage performance attenuation gradient data to obtain a battery cell balancing intervention strategy; Step S4: performing automated firmware design based on the battery cell balancing intervention strategy to obtain balancing intervention strategy firmware; embedding the balancing intervention strategy firmware into the battery cell management system to perform balancing protection of the battery cells.

2. The method for balancing and protecting multiple battery cells connected in series according to claim 1, characterized in that: The calculation of the internal resistance failure probability based on the internal resistance change series gradient influence data includes the following steps: The influence range difference between different nodes is calculated for the node thermal damage influence range data to obtain the influence range difference data; Perform thermal interaction internal resistance influence correction on the internal resistance change series gradient influence data according to the influence range difference data to obtain internal resistance change series correction data; Calculate the node internal resistance failure limit based on the impact range difference data and the internal resistance change series correction data to obtain the node internal resistance failure limit data; The cross-validation method is used to perform data cross-segmentation validation on the node internal resistance failure limit data to obtain the internal resistance failure limit segmentation validation data; Based on the law of large numbers, the internal resistance failure probability is calculated for the internal resistance failure limit segmentation verification data to obtain the internal resistance failure probability data.

3. The method for balancing and protecting multiple battery cells connected in series according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: performing intervention state identification according to the thermal damage performance attenuation gradient data and the internal resistance change series gradient influence data to obtain battery cell intervention state identification data; Step S32: Formulate a battery cell balancing intervention strategy based on the battery cell intervention state identification data, the thermal damage performance attenuation gradient data, and the internal resistance change series gradient influence data to obtain a battery cell balancing intervention strategy.

4. The method for balancing and protecting multiple battery cells connected in series according to claim 3, characterized in that: Step S31 includes the following steps: Step S311: Calculate the node thermal damage depths between different nodes according to the thermal damage performance attenuation gradient data and the internal resistance change series gradient influence data to obtain the node thermal damage depth data; Step S312: evaluating the impact weight of the series node line according to the node thermal damage depth data and the series gradient impact data of the internal resistance change, and obtaining the node line impact weight evaluation data; Step S313: calculating the bearing range of the series structure between different nodes according to the node thermal damage depth data and the series gradient influence data of the internal resistance change to obtain the bearing range of the series structure; Step S314: using a preset node limit state identification model to perform intervention state identification on the node line influence weight assessment data and the series structure load-bearing interval to obtain battery cell intervention state identification data.

5. The method for balancing and protecting multiple battery cells connected in series according to claim 3, characterized in that: Step S32 includes the following steps: Step S321: identifying high-risk areas according to the battery cell intervention state identification data, the thermal damage performance attenuation gradient data, and the internal resistance change series gradient influence data, and obtaining series node high-risk area data; Step S322: using the SARSA algorithm to perform environmental state perception on the high-risk area data of the series nodes to obtain high-risk environmental state perception data; Step S323: adjusting the node power output according to the high-risk environment state perception data on the thermal damage performance attenuation gradient data and the internal resistance change series gradient impact data to obtain node power output adjustment data; Step S324: formulating a battery cell balancing intervention strategy based on the high-risk environment state perception data and the node power consumption output adjustment data to obtain a battery cell balancing intervention strategy.

6. A balancing protection system for multiple series-connected battery cells, characterized in that: Used to perform the balancing protection method for multiple battery cells connected in series as claimed in claim 1, the balancing protection system for multiple battery cells connected in series comprises: A series gradient influence analysis module is used to collect multi-node battery operation status data of multiple series-connected battery cells to obtain multi-node battery operation status data; perform series gradient influence analysis on the multi-node battery operation status data to obtain series gradient influence data of internal resistance change; The performance attenuation gradient calculation module is used to simulate the thermal damage gain effect according to the series gradient influence data of the internal resistance change to obtain the thermal damage gain effect data; the performance attenuation gradient is calculated according to the thermal damage gain effect data to obtain the thermal damage performance attenuation gradient data; A balancing intervention strategy formulation module is used to formulate a balancing intervention strategy for the series battery cells according to the thermal damage performance attenuation gradient data to obtain a balancing intervention strategy for the battery cells; The intervention strategy execution module is used to perform automatic firmware design based on the battery cell balancing intervention strategy to obtain the balancing intervention strategy firmware; and embed the balancing intervention strategy firmware into the battery cell management system to execute the balancing protection of the battery cell.

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